DocumentCode
3413831
Title
Optimum Unit Commitment for Thermal Power Plants - A Genetic Algorithm Approach
Author
Bedekar, Prashant P. ; Bhide, Sudhir R. ; Kale, Vijay S.
Author_Institution
Electr. Eng. Dept., Visvesvaraya Nat. Inst. of Technol., Nagpur, India
fYear
2009
fDate
18-20 Dec. 2009
Firstpage
1
Lastpage
4
Abstract
This paper presents genetic algorithm method for unit commitment of thermal power plant. The optimum allocation of generations (for a given plant load) to different units of a plant is called unit commitment (UC). It can be easily shown that the optimal operation of the units at a thermal power station can be achieved when the incremental fuel cost (incremental cost) of all the units are equal. A new fitness function is defined in this paper, which combines (i) equal incremental cost (IC) criteria, and (ii) generation and load balance constraint. Generation of each unit is taken as variable. The minimum and maximum generation limits of the units are incorporated with the help of lower and upper bounds of variable. The genetic algorithm (GA) optimization method is employed to estimate the optimum allocation of generations to different units of plant, making use of the fitness function. Computer programs (using MATLAB) have been developed for optimum UC, using GA technique.
Keywords
genetic algorithms; power generation dispatch; power generation scheduling; thermal power stations; GA optimization; MATLAB; equal incremental cost; fitness function; genetic algorithm; incremental fuel cost; load balance; optimum allocation; optimum unit commitment; thermal power plants; Constraint optimization; Cost function; Fuels; Genetic algorithms; Optimization methods; Power generation; Power generation economics; Power system economics; Thermal engineering; Upper bound;
fLanguage
English
Publisher
ieee
Conference_Titel
India Conference (INDICON), 2009 Annual IEEE
Conference_Location
Gujarat
Print_ISBN
978-1-4244-4858-6
Electronic_ISBN
978-1-4244-4859-3
Type
conf
DOI
10.1109/INDCON.2009.5409369
Filename
5409369
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